Toward Optimal Remote Radio Head Activation, User Association, and Power Allocation in C-RANs Using Benders Decomposition and ADMM

To satisfy the rapidly growing demands of wireless communications, new structures have been proposed for the fifth-generation (5G) mobile communication networks, such as cloud radio access networks (C-RANs), which have advantages including high energy efficiency, large network capacity, and high fle...

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Veröffentlicht in:IEEE transactions on communications Jg. 67; H. 7; S. 5008 - 5023
Hauptverfasser: Wu, Zhikun, Fei, Zesong, Yu, Ye, Han, Zhu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Activation
Algorithms
alternating direction method of multipliers
Array signal processing
Benders decomposition
Cloud radio access networks
Communication networks
Computer simulation
Convex functions
Decomposition
Iterative algorithms
Iterative methods
Mathematical programming
max-sum algorithm
Mobile communication systems
Nonlinear programming
Optimization
power allocation
Power management
Radio
Resource management
Signal processing algorithms
Simulation
Solvers
user association
Wireless communication
Wireless communications
Wireless networks
ISSN:0090-6778, 1558-0857
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Zusammenfassung:To satisfy the rapidly growing demands of wireless communications, new structures have been proposed for the fifth-generation (5G) mobile communication networks, such as cloud radio access networks (C-RANs), which have advantages including high energy efficiency, large network capacity, and high flexibility. This paper concentrates on the problem of remote radio head (RRH) activation, user association, and power allocation in C-RANs. To tackle the problem with <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula> norm, we transform it into a mixed-integer nonlinear programming (MINLP) problem. Instead of solving it by centralized solvers, we propose a novel algorithm based on Benders decomposition, which can obtain the optimal solution of the MINLP problem. To solve the primal problem in Benders decomposition efficiently, we adopt the alternating direction method of multipliers (ADMM) to achieve a parallel implementation. To further reduce the complexity of solving the MINLP problem, a distributed two-stage iterative algorithm combining the ADMM and the max-sum algorithm is also proposed. The simulation results demonstrate that the first proposed algorithm can obtain the optimal solution, and the second proposed algorithm outperforms conventional algorithms significantly.
Bibliographie:ObjectType-Article-1
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2019.2904268